How Do AI Agents Work? A Complete Guide

How do AI agents actually work?

A lot goes into building AI agents. And, in fact, factors such as the choice of LLM model, configuration, orchestration patterns, guardrails, and the design of these autonomous software programs influence their operations. While there are a lot of moving parts under the hood, we'll try to explain how AI agents work after developers have deployed them for human users?

The reason–act–observe loop

While AI agents execute tasks autonomously (without constant human interruption or intervention), they still require a human to set goals and define rules and permissions, which together provide the boundaries within which they operate. Once developers present the agent's predefined rules, you, as the user, can set the goal by writing a prompt. The agent then creates an action plan to achieve the specified goals, decomposes the tasks into subtasks, and considers the available information. This process is known as reasoning. 

If the tasks are simple enough and can be solved using the information already available in its context or memory. But today, enterprises and individual users deploy AI agents to solve far more complex problems. In such cases, the AI model takes extra steps: it relies on the reasoning stage to determine which external tools to use to gather data and refine its responses. It then gathers data from scraped pages, database records, and other AI agents to gain additional context. 

Next comes the observation stage. The agent evaluates the new information, decides whether it's relevant, and stores useful context in memory before determining whether another iteration of the loop is needed. If the new information is relevant, the model generates a response. If not, the agent uses this information to refine its reasoning, kickstarting the reason-act-observe loop that only stops when there’s enough information and context to generate a final response.

Reasoning frameworks

Reasoning and acting form the basis of the ReAct framework. This framework allows LLMs to interact with the external environment and external tools, thereby facilitating complex problem-solving. With ReAct, the LLM creates and updates plans of action while simultaneously interacting with and using information from external tools or environments into its reasoning. It then observes what it generates from incorporating the new information before considering whether that response achieves the goal. This makes the ReAct framework quite resource-intensive due to the potential for back-and-forth.

ReAct isn't always the best choice, though. This is especially true because the observation requirement can induce an endless loop that increases costs. In such applications, reasoning without observation (ReWOO) is used. It decouples reasoning from observation, enabling the AI agent to think around the problem before attempting to solve it.

The five components that keep the loop running

The loop comprises five components:

1. LLM: As we mentioned earlier, LLMs are the foundation upon which AI agents are built. Besides handling natural language processing, the LLM also defines the logic for how the AI agent will solve the user’s problem. In most cases, the logic involves creating a plan, decomposing it into smaller, manageable tasks, identifying bottlenecks, and determining how to readjust the plan (if necessary) or when to seek external help. It is the model that decides when to call the tools to gain external context or gather up-to-date information.

2. Memory: An AI agent with memory stores past interactions and information, allowing it to identify patterns as well as improve decision-making, performance, and perception.

3. External tools: External tools provide information from websites, APIs, databases, and other external systems that help the agent analyze data and complete tasks. The external tools include search engines, websites, APIs, external datasets and databases, and software such as calculators, weather apps, and maps, to mention a few.

4. Planning: Essential to solving complex queries, planning involves breaking down complex goals into smaller, executable tasks and arranging them logically for execution.

5. Retrieval: The process of gathering the right information from external sources. It's executed during the reasoning stage of the loop, since it's at this stage that the AI agent decides whether it requires external information, what information is required, and when it has collected sufficient information to act.

The context layer: What fills the agent's "working memory"

The context layer is the part of an AI agent that makes decisions and remembers the information needed to maintain context at any given time. Put simply, this layer decides what information to retain or forget over time and when to acquire and retain it. It can be described as the AI agent’s or LLM’s short-term and long-term working memory and reference system.

Because the context layer is so important, a practice known as context engineering has emerged. It refers to the methods used to provide an AI agent with the right information at the right time. 

But context engineering is not always effective, especially when the input context is too long for the LLM to process effectively, leading to a gradual dip in performance. This is known as “context rot” and models tend struggle when important information is buried in the middle of a long prompt instead of being placed near the beginning or end, making context organization just as important as context size.

What are the types of AI agents?

There are several types of AI agents, including:

1. Research agents: These AI agents plan searches, read data sources, synthesize data, and automate repetitive tasks such as producing citations. They rely on web search grounding to ensure the relevance of the data they provide. Elicit Research Agent is an example of an AI research agent.

2. Browser agents: Browser agents navigate websites, click through pages, fill in forms, and extract information by interacting with web pages like a human user. While they're becoming increasingly capable, some still struggle with challenges such as CAPTCHAs, complex login flows, and payment steps, which often require human intervention or specialized infrastructure. A good example is Oxylabs AI Studio Browser Agent.

3. Coding agents: Such AI agents can perform multi-step software engineering tasks, including coding features, debugging, handling merge requests, testing, and more. However, the 2025 Stack Overflow survey shows that only 30.9% of developers use AI agents at work. Examples include Cursor, Codex, and Claude Code.

4. Sales/GTM agents: These agents automate routine tasks such as lead enrichment, prospect research, outbound sales, and CRM updates. Landbase is an example of an agentic GTM AI system.

While many current agentic AI systems promise to deliver results with significant cost savings, not all of them can be integrated into business processes. The best autonomous systems that work reliably in production are those with dependable access to current, structured web data. This access provides grounding an eliminates the largest current artificial intelligence bottleneck.

Why web context is the real bottleneck for AI agents

Modern AI agents don't fail because they can't generate text. They fail because they don't have access to the right information at the right time. Large language models are usually pretrained long before they are released to the public. 

As a result, their training data has a knowledge cutoff, which is the date after which no new data was added to the training material. While knowledge cutoff helps the developers to fine-tune, evaluate, and optimize the model, it has a clear downside. It makes the training knowledge irrelevant when a user asks a question that requires the AI agent to use real-time context and information.

Websites, meanwhile, provide reliable sources of real-time information and context because they are regularly updated and the world wide web is just ever-expanding source of new data. Similarly, APIs provide access to live data from transactional systems, social media feeds, weather platforms, stock exchanges, and more, for example. This means that AI agents that are incapable of accessing and using these external tools are frozen in time and cannot make decisions relating to current or recent events, time-sensitive matters, or real-time prices.

Why AI agents fail and how to make them more reliable

A Gartner study predicts that more than 40% of AI agents will be canceled by the end of 2027, underscoring the scale of failure among otherwise promising tools intended to reduce human oversight. The study cites several reasons behind this failure, including:

1. Reliance on the model's internal knowledge and training data, which can lead to hallucinations and inaccurate responses. Grounding an AI agent with reliable, real-time external data helps reduce these issues.

2. High cost of deploying autonomous AI agents at scale, which results in incomplete or brittle deployment

3. Use of AI tools that lack agentic capabilities and, therefore, cannot handle tasks autonomously. According to Gartner, this failure arises from vendors falsely rebranding AI tools, such as chatbots and AI assistants, as full-on AI agents. 

4. Uncertainty of where and how to apply the AI agents, leading to misapplication that then leads to a broader-scope systematic failure.

5. The complexity of deploying autonomous agents at scale can result in misconfigurations where an AI agent breaks or starts showing slow response times.

6. Use of immature LLMs that are incapable of autonomously achieving complex business goals.

To improve agent reliability is to ground them with fresh, structured web data instead of relying solely on the model's training. Real-time web data access solutions like Oxylabs Web Scraper API can be easily integrated in to your agentic workflows through Model Context Protocol (MCP) while official Oxylabs Agent Skills can teach your model to use such tools with complete confidence in their code and use them as efficiently as possible.

Wrapping up

Specialized AI agents are built on LLMs and they are designed to create a plan of action and break it down into smaller, manageable and well-defined tasks. They also assess whether the available information, stored in memory, can solve complex problems and, if not, identify the external tools to find it. Lastly, they decide whether the collected information is relevant to the user’s query and use that to solve the problem or improve its decision-making basis.

As agent technology continues to evolve, we can expect AI agents to become more capable to tackle complex tasks, but they'll still depend on reliable data, thoughtful design, and human oversight to deliver consistent results.